4, and Table 1, is the generic metric for evaluating a partial derivation d which has two implementations (partial bleu or potential bleu).

they evaluate a partial translation through extending it to a complete translation by re-decoding, and thus they need many passes of decoding for many partial translations, while ours only need one pass of decoding for all partial translations and thus is much more efficient.


1 For example, the gender of the employee Kay Mann was marked as unknown in their gender assignment.

if the name field contains three words but no comma, choose the first and second words (again, a likely first and middle name).


(4), using a fixed point iteration (Minka, 2000).

the reason for the high perplexity of the improved model is that it does not consider the dynamics.


(2011) show that it works well at the point of semantic coherence of topics compared with LDA.

when we apply LDA to tweets, each tweet is treated as a single document.


Topic models, such as latent Dirichlet allocation (LDA) (Blei et al., 2003) are widely used to identify latent topic structure in large collections of documents.

it cannot efficiently analyze the large number of tweets generated everyday.


We then check each one to see whether it is related with any of the seven types listed in the table.

we devised a set of effective seed words and trigrams, mined from a dataset of secrets.


For both tasks, we provide guidelines mostly in the form of examples and intuition, motivated on one hand by the annotators not having specialized knowledge, and on the other hand by the observation that the literal-figurative distinction is subjective.

our starting point is a new large dataset of comparisons extracted from product reviews and annotated for figurativeness.


For each representation, two classification schemes are applied: a) FDA (Eq.

li and Sporleder (2010) assume that literal and figurative data are generated by two different Gaussians, literal and non-literal and the detection is done by comparing which Gaussian model has a higher probability to generate a specific instance.


Our model captures the local semantics and thus is capable to identify semantic outliers (=idioms).

m D represents the original "text" term by document matrix, while mD represents the "topic" term by document matrix.


Disambiguation Figure 10 shows a generated scene for the input "There is a room with a poster bed and a poster".

early work on the SHRDLU system (Winograd, 1972) gives a good formalization of the linguistic manipulation of objects in 3D scenes.


In this paper, we demonstrate that it is feasible to discover the coherent structure of a text using distributed sentence representations learned in a deep learning framework.

such deep connectionist architectures learn a dense, low-dimensional representation of their problem in a hierarchical way that is capable of capturing both semantic and syntactic aspects of tokens (e.g., (Bengio et al., 2006)), entities, N-grams (Wang and Manning, 2012), or phrases .


We also thank the three anonymous EMNLP reviewers for helpful comments.

in the recurrent framework, long-distance dependencies are difficult to capture due to the vanishing gradient problem (Bengio et al., 1994); two tokens may be structurally close to each other, even though they are far away in word sequence 3 .


Our rerankers can exploit the complete DT structure using TKs.

the latter allows us to represent structured data using the substructure space thus capturing structural dependencies between Dt constituents, which is essential for effective discourse parsing.


This research is part of the Interactive sYstems for Answer Search (Iyas) project, conducted by the Arabic Language Technologies (ALT) group at Qatar Computing Research Institute (QCRI) within the Qatar Foundation.

rhetorical Structure Theory (rST) (Mann and Thompson, 1988) represents the coherence structure of a text by a labeled tree, called discourse tree (DT) as shown in Figure 1.


13 In summary, the results in Tables 1 and 2 demonstrate that a k-best reranker can potentially improve the parsing accuracy at the sentence level, but may not be a suitable option for improving parsing at the document level.

supervised learning was first attempted by Marcu (2000b) to build a shiftreduce discourse parser.


Note that more important relational features would be the subtree patterns extracted from the DT.

since this does not improve the accuracy, we speculate that complete fragments, e.g., [CAUsE [ATTRI-BUTION][ELABORATION]] are more meaningful than the partial ones, e.g., [CAUsE [ATTRIBU-TION]] and [CAUsE [ELABORATION]], which may add too much uncertainty on the signature of the relations contained in the DT.


DISCTK can be used for any classification task involving discourse trees.

notice that DISCTK only works on a single pair.


Recent attempts (e.g., (Hernault et al., 2010b;Feng and Hirst, 2012;Joty et al., 2013)) are still considerably inferior when compared to human goldstandard discourse analysis.

here we illustrate how we compute the derivative of J multi (e i , e j ) with respect to different parameters.


as for the three measures, no system achieves top performance on all three, though some systems outperform all others for one of the measures.


We analyze errors of the four systems presented in the previous section on the CoNLL'12 English development data.

we use this information to build spanning trees for system entities: these spanning trees consist of exactly the edges which correspond to anaphor/antecedent pairs in the system output.


We therefore propose to employ a structured entity representation, which explicitly models links established by the coreference relation between mentions.

we represent an entity e over mentions {m 1 , .


Asher and Lascarides (1998) and Cimiano (2006) model bridging by integrating discourse structure and semantics from a formal semantics viewpoint.

in the decoding stage, the best first strategy (Ng and Cardie, 2002) is used to predict the bridging links.


Bridging resolution is an extremely challenging task in ISNotes.

for more details about the whole system, see Vieira and Poesio (2000).


Table 4 shows all the features we use for recognizing bridging anaphora. "

for each a ∈ A ml , we predict the bridging link to be the most confident pair (a, c ante ) among all instances with the positive prediction.


We report an F-measure of 0.42 for bridging anaphora recognition.

we think the coreference resolution system and the evaluation metric for coreference resolution are not suitable for bridging resolution since bridging is not a set problem.


1(d)) which simply forces user to use locational references.

because we are using generic intents as categorical features instead of specific intents, and a slot value match feature instead of domain specific slot types as features, our models are rather domain independent.


The models are considerably better at resolving explicit referrals (both non-spatial and spatial) compared to implicit ones.

implicit Referential : The user refers to the item using distinguishing features other than the title, such as the release or publishing date, writers, actors, image content (describing the item image), genre, etc.


For our dialog system, we build one SLU model per each domain to extract two sets of semantic attributes from utterances: user's intent and semantic slots based on a predefined semantic schema (see examples in Table 2).

the models which combine different features as apposed to individual feature set (the above the line models versusu below the horizantil line models) are statistically significant (based on the student t-test p¡0.01).


Third, previous studies have shown the difference in classifying Chinese discourse relations from English (Xing, 2001;Huang and Liao, 2011).

besides, the original 24 discourse relations defined by Mann and Thompson (1988) are further divided into a set of 18 relation classes with 78 finer grained rhetorical relations in RST-DT.


It is worth noting that the majority of discourse relations in Chinese are implicit, so the insertion of a connective in an implicit position can significantly ease the understanding of the discourse.

we determine the overall level first and then the analysis goes on step by step to the individual EDUs.


The search starts at the initial state s 0 = I(x) (see Algorithm 1).

note that this approach is myopic in the sense that F prune is learned without considering the implications for learningF score .


We propose a novel search-based approach for greedy coreference resolution, where the mentions are processed in order and added to previous coreference clusters.

our work is orthogonal to this line of work, but domain constraints and rules can be incorporated into our model as done in .


Recall that our features φ(s, a) for both pruning and scoring functions are defined over state-action pairs, where each state s consists of a set of clusters and an action a corresponds to merging an unprocessed mention m with a cluster C in state s or create one for itself.

coreference resolution is a structured prediction problem where the set of mentions m 1 , m 2 , • • • , m D extracted from a document cor-reponds to a structured input x and the structured output y corresponds to a partition of the mentions into a set of clusters c 1 , c 2 , • • • , c k .


This leads to a highly imbalanced learning problem because our model needs to learn (the weight of the single indicator feature) to predict NEW as the best action for a large set of mentions, which will bias our model to predict large number of NEW actions during testing.

then for all learning problems, min Fscore E(F score , F score ) ≥ min (Fprune,Fscore) E(F prune , F score ).


An alternative approach is to convert text descriptions to pre-specified representations using annotated training data, commonly used in language grounding tasks (Matuszek et al., 2013;Kushman et al., 2014).

we adopt a reinforcement learning framework and formulate game sequences as Markov Decision Processes.


Finally, we partition the data into training, validation, and test set, using a 60-20-20 percent split.

in this section we take a closer look at what kind of information we are able to learn, what is beyond the scope of our model, and what are the differences between the entity representations in WORD2VEC and the ones our model produces.


The broader goal of getting at referential information with distributional semantics is shared with Herbelot (2015).

finally, we share our methodology with work on mapping between corpus-based word representations and other representational spaces, such as subject-generated concept properties (Johns and Jones, 2012;Hill et al., 2014;fȃgȃrȃşan et al., 2015), visual features (frome et al., 2013;Socher et al., 2013b;Lazaridou et al., 2014) or brain signals (Mitchell et al., 2008;Murphy et al., 2012).


For efficient training and decoding, we add a restriction to DGSG: each dependency-graph fragment covers a continuous span of the source sentence.

the translation process is rather complex and the resources it relies on, namely abstract meaning corpora, are limited as well.


Third, rewriting the target sentence requires the output to be grammatical (for it to be used as reference translation), which is not a concern when rewriting source sentences.

we evaluate our approach using standard machine translation data (the Reuters newsfeed Japanese-English corpus) in a simultaneous translation setting.


a typical sentiment classifier misclassifies this as negative because miss and sad express sadness.


Brand preference analysis is an important topic in marketing.

finally for each user, we collected her preferences for 22 brands in six categories.


Here to limit the time MTurkers need to spend on the survey, instead of the full 300-item personality test, we used the shorter 50-item IPIP survey (Goldberg, 1993) which will score a user along 5 general personality dimensions: openness, conscientiousness, extraversion, agreeableness and neuroticism.

there are several main findings from these results.


Since the majority of MTurkers are not active Twitter users, to increase the size of our data, in addition to MTurk, we also directly invited random Twitter users to participate in our TAE survey.

scores that were derived by our system correlated with survey-based scores for 80.8% of participants' Big Five scores (p<0.05 and correlation coefficients between 0.05 and 0.75), for 86.6% of participants' Needs scores (p<0.05 and correlation coefficient between 0.05 and 0.8), and for 98.21% of participants' Values scores (p<0.05 and correlation coefficients between 0.05 and 0.55).


For Cadillac, the top trait is hedonism, which is often associated with people who pursue pleasure and sensuous gratification in life.

there was barely any improvement for Target.


We exploit both Wikipedia edits and page views for annotation.

context Similarity For entities that are not directly linked to mentions (the mention similarity is zero) we exploit external resources instead.


Event Mining Using Wikipedia Recently some works exploit Wikipedia for detecting and analyzing events on Twitter (Osborne et al., 2012;Tolomei et al., 2013;.

this work was funded by the European Commission in the FP7 project ForgetIt (600826) and the ERC advanced grant ALEXANDRIA (339233), and by the German Federal Ministry of Education and Research for the project "Gute Arbeit" (01UG1249C).


Instead of inspecting all hashtags in our corpus, we follow Lehmann et al.

the authors only aim to retrieve entities directly mentioned within a hashtag, which are very few in practice.


Entity Relatedness In this work, we assume that an entity endorses more of its influence score to highly related entities than to lower related ones.

this coherence does not hold for topics in hashtags: Entities reported in a big topic such as the Olympics vary greatly with different subevents.


In the first step, we generate combined candidate summaries (Section 4).

the advantage of the latter oracles over ICSISumm is much larger: on average 0.060/0.022 and 0.039/0.034 (R-1/R-2).


Results reveal superior variants of metrics distinct from previously best recommendations.

in MT, recent work has identified the suitability of Williams significance test (Williams, 1959) for evaluation of automatic MT metrics Graham, 2015), and, for similar reasons, Williams test is suited to significance testing differences in performance of competing summarization metrics which we detail further below.


aVGMaX then takes the average of those n maximum similarites; MaXMaX takes the maximum.


To alleviate the issue of low coverage, a large body of work has been dedicated to lexicon learning from more abundant and less restricted comparable data, e.g., (Fung and Yee, 1998;Rapp, 1999;Gaussier et al., 2004;Shezaf and Rappoport, 2010;Tamura et al., 2012).

the results appear to indicate that adding such additional information does not have a clear effect for genuine similarity, but may lead to a small performance increase for relatedness.


Table 1 present the results obtained for both ratings based and polarity classifier on RHMR and MRD Dataset.

this presents difficulty in assign-ing a single polarity/rating to the review.


We calculate the E * sem (t|s; θ s t ) in similar manner.

the margin of accuracy obtained against next best classifier is about 8%.


We have used the 200 test documents that are manually summarized as gold standard data for ROUGE evaluation.

the main reason being that the functions with optimal values of trade-off parameter α strike out a balance between relevance and subjectivity.


For an opinion y automatically extracted by a span-based system, both the SVM baseline and PSL assign scores to ETARGET(y,t).

in both cases, the score assigned to the ground atom is 1.0.


Further, our previous models all require certain manual (oracle) annotations to be input.

aGENT(x,a) and THEME(x,h): We consider all nouns in the same or in sibling constituents of a +/-effect event as potential agents or themes.


SOURCE(y,s): S1 extracts the source of each opinion, S2 does not extract the source, and S3 assumes the source is always the writer.

in MPQA 3.0, both attitudes and ESEs have eTarget annotations.


Then it is normalized as input into PSL.

the second and third evaluations are carried out on the entire corpus.


For this analysis, we looked at the simile component triples as well as the context of ten tweets in which the simile appeared.

for positive polarity, we use similes labeled positive as positive training instances, and similes labeled negative or neutral as the negative training instances.


Comparing these results with Table 4, we see that there is still a gap between the performance of classifiers trained with manually annotated data versus automatically acquired data.

for this purpose, we combined the MPQA (Wilson et al., 2005), and the AfINN lexicon (Nielsen, 2011) to use as our sentiment lexicon.


This can potentially bias the training data and limit the classifier's ability to learn to recognize affective similes that do not contain words with a positive or negative connotation.

but because sentiment resources have limitations (e.g., sentiment classifiers are not perfect, sentiment lexicons do not possess knowledge of context), these instances will have some noise.


Automatically Acquired Training Data Table 5 shows the performance of the classifiers (using our full feature set) when they are trained with automatically acquired training instances.

we explore an additional approach where instead of judging the sentiment of the words in the simile, we analyze the words in the tweet surrounding the simile.


We train classifiers using lexical, semantic, and sentiment features, and experiment with both manually and automatically generated training data.

it is an excellent source for collecting similes that people use in everyday conversation.


Therefore, optimization methods for IBM Model 1 (specifically, the EM algorithm) are typically only guaranteed to reach a global maximum of the objective function (see the Appendix for a simple example contrasting convex and strictly convex functions).

for the d parameters, we use λ = 16 throughout.


We begin by reviewing IBM Model 1 and introducing the necessary notation.

we have that the posterior probabilities of this model factor just as those of the standard Model 1 and we have an M step that requires optimizing � a (k) q(a (k) |e (k) , f (k) ) log p(f (k) , a (k) |e (k) ) 1: Input: Define E, F , L, M , (e (k) , f (k) , l k , m k ) for k = 1 .


The best score is 92.96% for the PC removal, and 92.46% for the Caron p-transform, both using the full data set.

it is common practice to reduce the dimensionality of an n-dimensional space to as many PCs as it takes to cover 80% of the total eigenvalue mass.


Figure 3 shows how the redistribution affects the different relations in the BLESS test.

this result has not been replicated and remains poorly understood.


While its raw application to language modeling did not achieve clear improvement over the word-based models (Mikolov et al., 2012), this approach shown impressive results for text generation (Sutskever et al., 2011;Graves, 2013).

all the models are implemented 4 with the Theano library (Bergstra et al., 2010).


Designing subword-level systems is appealing for several reasons.

for this line of work, the main issue is to learn long range dependencies at the character level since the word level is not considered by the model.


The band members originally came from different death metal bands, bonding over a common interest in d-beat.

we attach the document vector directly to the output layer, in parallel with the recurrent hidden component.


In this paper, we explored an alternate architecture for embedding-based neural network models which allows for a fully pre-computable network with multiple hidden layers.

two algorithms have been proposed to achieve this: (1) noise-contrastive estimation (NCE) (Mnih and teh, 2012;Vaswani et al., 2013) and (2) explicit self-normalization (Devlin et al., 2014), which is used in this paper.


The best configurations of "Jointly(anchor)" and "Jointly(desp)" are exactly the same: k = 100, α = 0.025, c = 10, s = 5.

the aim is to learn continuous vector representations (embeddings) for entities and relations of a structured knowledge base (KB) such as Freebase.


As reported in the paper, using entity names severely pollutes the embeddings of words.

we believe that the good performance of "Jointly(desp)" is due to the appropriate design of the alignment mechanism.


w i,n is the n th word in the description of e i .


The dual methods were particularly sensitive to these hyperparameters, performing poorly if they were not chosen carefully.

in practice, how fast each method converges varies across tasks.


Additionally, we used features including the difference in noun chunk character and token length, the difference in number of tokens, shared named entities, and subtask names.

highAgree does outperform Integrated on 4 or the 5 tasks, especially for hard Cases: hard Case improvements for Biased Language and POS Tagging, and Affective Text, and overall improvements for RTE, POS Tagging, and Affective Text were significant (Paired TTest, p < 0.05, for numerical output, or McNemar's Test 10 (McNemar, 1947), p < 0.05, for nominal classes).


All embeddings perform better than guessing, indicating that there is at least some coherent structure captured in all of them.

assume we use rank correlation as our metric.


Both are common areas of interest across many machine learning formalisms: probabilistic logic (Bach et al., 2015), graph algorithms (Low et al., 2012), and probabilistic grammars (Cohen et al., 2008).

logic-lDA: encodes general domain knowledge as first-order logic and incorporates it in lDA (Andrzejewski et al., 2011).


It assumes that there is a one-to-one mapping between topics and labels, and it restricts each document's topics to be sampled only from those allowed by the documents label set.

a correlation between two words v and w indicates that they have a similar topic distribution, i.e., p(z|v) ≈ p(z|w).


Moreover, the document-topic count is also sparse, which fits into the efficient learning framework.

information about documents (Ramage et al., 2009) or words (Boyd-Graber et al., 2007) can improve LDA's topics.


For example, a scientific paper and the prior work it cites might have similar topics (Dietz et al., 2007) or friends in a social network might talk about the same topics .

inference is cumbersome for LDA models with prior knowledge.


Many models have been proposed for knowledge base completion, including those reviewed in Section 3.4 (Nickel et al., 2011;Bordes et al., 2013;Yang et al., 2015;Socher et al., 2013).

previously proposed training objectives for knowledge base completion only train on queries of path length 1.


It returns the top m highest scoring trees in D (in our experiments we used m = 200, 000), where the score for each tree is the perceptron-based score normalized by the sentence length.

when using multiple source languages, average accuracy is improved to 82.18%.


During training, the languagespecific errors are back propagated through different branches according to the language, guiding learning towards an interlingual representation that informs parsing decisions in both languages.

building a dependency parser for a resourcepoor language usually starts with the delexicalized parser and then uses other resources to refine the model.


To understand this pattern of performance differences for the cascade versus the joint model, one needs to consider the cascade model formulation.

with more data, the model is better able to learn the tagset mapping as part of joint training.


Both the LSTM systems we introduced and the baseline require a training set of a considerable size.

we leave experiments with paraphrasing models to future work.


There is no result reported for the syntactic model beyond 1 million training sentences, because training becomes infeasibly slow 6 .

table 5 shows that the performance on cross-domain word ordering cannot reach that of in-domain word ordering using the syntactic models.


This can be explained by the local scoring nature of the N-gram model.

as can be seen from the figure, the syntactic model is much slower to train.


We use the AFP news data as the training data for the experiments of this section.

this type of syntactic language models are typically used jointly with N-gram model for text-to-text tasks.


We also include several baselines that have access to the same training data as our system.

in this work we focus on factored scoring functions, s, that take into account a fixed window of previous words: where we define y c y [i−C+1,...,i] for a window of size C. consider the conditional logprobability of a summary given the input, s(x, y) = log p(y|x; θ).


We observe relative consistency between different rouge scores for each summarization approach.

• Iterative: We select top sentences iteratively from each group until we reach the summary length threshold.


Summarization approach is based on either community detection (Citation-Context-Comm) or discourse model of the article (Citation-Context-Disc) and sentence selection strategy can be iterative (It) or by relevance and diversification (Div).

citations may not accurately represent the content of the referenced article as they are biased towards the viewpoint of the citing authors.


In the community detection method, no pre-determined order is specified.

our goal is to leverage citationcontext in the reference article to correctly reflect the reference paper.


a document is represented by a vector, whose components reflect the weight of every distinct word contained in the document.


Second, we will work on using novel features for the creation of generalized models.

we also incorporated Named Entity Recognition (NER) for feature extraction.


We also see that BLRR is comparable to linear SVM regression, giving almost the same performance for prompts 4 to 7; slightly poorer performance for prompts 1 to 3; and much better performance for prompt 8.

this is unsurprising, since in SharedHyper the weights are effectively not shared between the target and source training examples: only the hyper-parameters α and λ are shared.


Some commonly used approaches include EasyAdapt (Daumé III, 2007), instance weighting (IW) (Jiang and Zhai, 2007), and structural correspondence learning (SCL) (Blitzer et al., 2006).

human graded essays are not readily available.


The training data consisted of all claims, where positive examples included claims for which at least one CDE of the relevant type existed in the labeled data and negative examples included all remaining claims.

for example, it is possible that a piece of text has the characteristics of a certain evidence type, but does not support the claim (see S4 in Table 1).


• Lexicons -including external lexicons (the Harvard IV-4 dictionary) and manually and automatically compiled in-house lexicons.

many econometric studies in recent years have supported the view that development aid has no effect on the speed with which countries develop.


We further require that a detected CDE is reasonably well phrased, and easily understandable in the given context, so that it can be instantly and naturally used to support the claim in a discussion.

for example, for a single paragraph with five sentences, our system will examine a total of 5 + 4 + 3 = 12 candidates.


For example, it is possible that a piece of text has the characteristics of a certain evidence type, but does not support the claim (see S4 in Table 1).

for each evidence type, we manually compiled a lexicon of words characterizing this type by looking at examples from the held-out data.


For quality, we compare the search result sets for the autocorrected queries with those of the reference and get an estimate of how similar each autocorrected query behaves to the expected behavior given by its gold correction.


Text features for predicting click probability There have been many studies on the use of text features for click prediction.

the user click feedback features capture the inclination of individual users to click on ads in general.


As noted by (Tai et al., 2015), this approach is a strong competitor to more complex models such as RNNs or LSTMs despite its simple composition method.

this observation confirms the findings in (Wang et al., 2013) about the significant role of certain ad expressions in triggering users' psychological desires.


With weight logloss, we aim to optimize sentence vectors instead of individual word vectors: where the component loss functionl (•, •, •) is defined as follows: where µ s returns the average vector for the multiword string s, i.e.

in all experiments below, we used the same personalized historical CTRs, contextual and lexical features as the baseline system described in Section 3.


On top of word embeddings, a new stream of research on sentence and paragraph embeddings has also emerged to tackle higher level tasks such as sentiment analysis, machine translation and semantic relatedness tasks.

it is clear that conventional word embeddings (which solely rely on word co-occurrence in a context window) can only offer limited discriminative power because queries and ad text are typically very short.


We consider a novel setting for Named Entity Recognition (NER) where we have access to document-specific knowledge base tags.

we only use the KB to collect aliases, but we could use it to harvest related entities.


The resulting KBs are generally static and are not updated as the facts change (Suchanek et al., 2007;Carlson et al., 2010;Fader et al., 2011;Mitchell et al., 2015).

extracting relational facts between entities and storing them in knowledge bases (KBs) has been a topic of active research in recent years.


DIEL predicts the class of a list with its feature vector, while LP-baseline determines the class of a list by checking its predicted class in the result of MRW with the corresponding training instances as seeds.

it attempts to classify each NP in the input corpus into one of the four types or "other" with the training seeds as distance supervision.


Another baseline is developed with the output of MRW LP (LP-baseline) that contains labeled lists and mentions.

here we describe a pipelined system which (1) identifies lists of semantically-related items using lexico-syntactic patterns (2) uses distant supervision, in combination with a label-propagation method, to find entity mentions that can be confidently labeled and (3) from this data, uses ordinary classifier learners to classify entity mentions by their semantic type.


"Get emergency medical help if you have chest pain, weakness, shortness of breath, slurred speech, or problems with vision or balance."


Unfortunately, such low knowledge density makes it challenging to use such KGs in real-world applications.

we observed that relation mapping has lesser accuracy due to two reasons.


Convolution To capture the local features around each node of the dependency path, we consider a fixed size window of nodes around each node in the window processing component, producing a matrix of node features of fixed size d w × 1, where d w = d × w and w is the window size.

in this paper, we propose to learn more robust relation representations from shortest dependency paths through a convolution neural network.


We admit that models with more complex structures can better handle these considerations.

any correspondence please refer to Yansong Feng.


Our corpus includes 1,890 messages sampled from Weibo between November 2013 and December 2014.

the use of embeddings for Chinese remains a challenge.


A simple example below shows a group of relations: X, resign from, Y X, had resigned from, Y X, finally resigned from, Y All linked tuples with the same simplified pattern form a group.

the latter one shows a more concrete representation of the relation, because deceased person covers small entities than person in the knowledge base.


One is a naive method which only considers the similarity between arguments and corresponding entities: The other method takes predicate paths between e 1 and e 2 into consideration.

we trust the factor of P ( p | w) only when there exists a similar predicate path.


In order to better capture non-trivial interactions between the features described above, we adopt a DNN with a bottleneck.

the features and the model for the context are important for efficiency.


The improvement over the baseline is 5.4 folds, and is statistically significant using a paired Student's ttest (p < 0.05).

we employed Conditional Random Fields (CRF) model (Lafferty et al., 2001) to identify key concepts, which are most in need of explanation by external education materials.


No preprocessing approaches like eliminating stop words and stemming, are used.

based on this idea, we seek the relation of texts written in different languages mediated by the similarity between images.


Having set Japanese as the query language, we retrieve documents written in English.

we focus on an extension of CCA, generalized canonical correlation analysis (GCCA), to handle more than two inputs (Kettenring, 1971).


These documents might be less subjective compared to CEO letters, but they are usually more comprehensive and contain interesting forwardlooking information.

the data are filtered according to specific countries and grouped by year respectively by bank.


This can potentially be attributed to an important and often-cited speech by ECB president Mario Draghi, who calmed the financial markets with the announcement to do "whatever it takes to preserve the Euro.

to the majority of the related papers, we only use specific sections of the annual reports, namely CEO letters and outlook sections (see Section 3).


Alone, these variants only occasionally do better than the sentiment flow patterns (b3).

then, we classify local sentiment with the algorithm of Socher et al.


Accuracy of predicting 3-class global sentiment for each combination of training and test domain using the baselines and/or a selection of the 16 evaluated model variants.

we hypothesize that, under an adequate model, similar sentiment flows express similar global sentiments, also across domains.


We skip these results due to space limitations.


One thing to note is that, our objective function is not differentiable because of the loss function l( x n , y n , Θ).

the first is targeted sentiment classification, assuming that entity mentions are given.


The gains on English are mostly attributed to improved recalls, while the precision of the neural CRF models are relatively lower.

the discrete model is based on manually defined binary features, which do not fire if not contained in the training data.


Topics in their model resemble targets in our work, although topics are represented by hashtags, which exists in 14.6% tweets and 27.5% subjective tweets (Wang et al., 2011).

thanks to the rich discrete features in parameter space, the integrated model does not rely on fine-tuning of word embeddings, which even caused slight overfitting and reduced the performances.


x n , our task is to predict a sequence of labels, y = y 1 .

e6 (25) errors were due to irrelevant entries in our restrictive word dictionary.


It may be argued that in example (4) the target users are restricted to those who are interested in the price.

the method proposed by O'Mahony and Smyth (2010) determines the helpfulness of a product review independent of the user profile and thus cannot recommend reviews based on userrelated attributes.


Kim and Hovy (2006) proposed a method to identify a reason for the evaluation in an opinion, such as "the service was terrible because the staff was rude".

the identification for U-CFOs facilitates predicting the review helpfulness (O'Mahony and Smyth, 2010;Moghaddam et al., 2012).


To focus specifically on the strengths of these models at producing informative sentence representations, we use sentence embedding as an intermediate step in the NLI classification task: each model must produce a vector representation of each of the two sentences without using any context from the other sentence, and the two resulting vectors are then passed to a neural network classifier which predicts the label for the pair.

we gratefully acknowledge support from a Google Faculty Research Award, a gift from Bloomberg L.P., the Defense Advanced Research Projects Agency (DARPA) Deep Exploration and Filtering of Text (DEFT) Program under Air Force Research Laboratory (AFRL) contract no.


For example, Q2 uses "someone" to refer to the finisher.

φ indicates that a field is either an empty string or equals "do/doing".


Together, these results suggest that non-experts can annotate much of the information contained in PropBank, and produce a more easily interpretable annotation.

a gold question-answer pair can be transformed into multiple positive training samples.


It propagates trust scores on the graph where each vertex denotes one document and an edge indicates the similarity between them.

two types of indexes are used: the document-to-word index and the wordto-document index.


Cut-off Value According to the globally comparable trust scores, we can (i) rank all of the documents, and trade off the performance metrics by choosing an appropriate cut-off value γ for various applications, or (ii) rank the documents of an individual entity separately and obtain its top-k mentions.

considering that the propagation begins with document d i and that we are required to find all of its out-neighbors o(d i ) through a traversal of the entire dataset, then the complexity is O(n 2 ).


true mention will receive a higher trust score and be ranked higher.

to traditional entity disambiguation tasks, TED in short texts can require as little information as a name list.


Generally, Relevance Model (RM) (Huang and Croft, 2009) refers to the probability distribution over all words conditioned on their occurrences in a set of previously-known relevant documents (or high-quality pseudo-relevant documents), i.e., , where V is the vocabulary, R is the document set, and P(w|R) can be estimated by TF-IDF.

the distribution is produced by densities within all interval ranges of relevance score.


Fuzzy matching (FM) relaxes the conditions to a large extent, allowing Q to be split into nonadjacent words.

this work was supported by the U.S. DARPA DEFt Program No.


A wide range of listwise learning methods can benefit from the idea of stochastic learning provided in this paper.

to the conventional ListNet, this improvement propagates to the results on the test data.


We selected 108 elementary-level science questions (non-diagram, multiple-choice) from 4th grade New York Regents exam as our benchmark (Dev-108) and used another 68 questions from the same source as a blind test set (Unseen-68) 7 .

when using only one KB rule, dropping FUP clauses has a bigger influence that dropping Acyclic constraint clauses.


G m contains a set of vertices representing the mentions extracted from the source document and a set of undirected edges.

take "A20", a type of protein as an example.


Then it compares each candidate graph with the mention context graph, and evaluates their vertex set similarity for context similarity ranking.

note that all of these loose settings comply with our intention of generating a light-weight source context representation born with domain and language independence.


Recent cross-lingual EL approaches can be divided into two types.

this improvement is reasonably substantial.


Not all relation paths are meaningful and reliable for learning.

from Table 4 we observe that: (1) PTransE outperforms TransE+Rev+Path significantly for relation prediction by reducing 41.8% prediction errors.


Hence, we add reverse relations for each relation in KBs.

here we demonstrate the performance of PTransE and some baselines with respect to different types of relations in Table 3.


We use NSE to evaluate performance of FIGMENT on entities that do not occur in subject position.

knowledge bases are therefore always in need of updates and corrections.


We plan to address this in the future (i) by running FIGMENT on larger corpora, (ii) by refining the FIGER type set to cover more Freebase entities, (iii) by exploiting a hierarchy over types and (iv) by exploring more complex input representations of the context for the CM.

the global model transforms MIML into a multi-label problem by merging all instances of an example.


Their method is based on KB information, more specifically entity descriptions in Wikipedia and Freebase.

we implement the global model and the context model as well as a simple combination of the two and call the resulting system FIGMENT: FIne-Grained eMbedding-based Entity Typing.


To address this problem, we present an information extraction method that can be used for knowledge base completion.

the other vector is the concatenation of the embeddings of the 2k words to the left and right of the mention.


Similar to the evaluation performed by Lin et al.

to most other work on knowledge base completion, we focus on fine-grained classification of entities as opposed to relations between entities.


Therefore, our approach comprises two successive stages: • A disambiguation stage (Section 5) where all KB i ∈ K are linked to S, either by inter-resource mapping (Figure 1(a)) or disambiguation ( Figure 1(b)), and all E i are merged into a unified set of entities E * .

a few contributions have tried to broaden the scope and include different resources at the same time, but with rather different goals from ours.


Our work leverages the factored approach, but improve performance by using a continuous-space sentence representation for predicting presence of a name.

the ST RNN sees an improvement in precision for all domains.


All other learning parameters are set to their default values.

employing sieves for spatial relation extraction obviates the need for such ad-hoc decisions.


The instances for training the remaining six classifiers are generated similarly.

it will be used in combination with the original 31 features.


These nodes are typically ambiguous first or last names.

an error analysis of our system revealed that majority of mistakes were due to the multiple names for a character not being merged into a single identity.


We described a general framework that can encompass the standard n-gram baseline approach as well as more relaxed versions with smoother word and phrase kernels.

moreover, we were able to get better results with only bigrams (small L).


Regarding the phrase kernels, PK generally produced better results than CK, implying that the semantic linearity and ontological relation encoded in the embedding is not sufficient enough and treating them separately is more beneficial.

in this paper, we proposed a novel convolutional sentence kernel based on word embeddings that overcomes the sparsity issue, which arises when classifying short documents or when little training data is available.


To prevent overfitting, we tuned the parameters using cross-validation on 80% of PL05 dataset (α = 5, λ 1 = 1 for PK since there is no need for distortion as the phrases are of the same length by definition, and λ 1 = λ 2 = 0.5 for CK) and used the same set of parameters on the remaining datasets.

we believe CK has more room for improvement with the use of more accurate phrase embeddings such as the ones from Le and Mikolov (2014), Yin and Schütze (2014) and Yu and Dredze (2015).


In this section, we present our SIMMR tree recipe parser.

• The number of distinct unigram matches.


Experimental results on document clustering across three corpora demonstrate that our model effectively incorporates prior topics, and significantly outperforms state-of-the-art DPMM model.

we model prior topics as known colors that have a certain probability proportional to α k to be assigned to a document.


We collect machine learning conference NIPS datasets composed of paper titles and abstracts from 2012 to 2014 -each year includes 342, 360 and 411 documents respectively.

it has freedom to discover new topics beyond prior topics, as well as to detect which prior topics are not covered by current data.


As a result, GRNN is a more powerful model which outperforms the plain GRNN.

this gate mechanism is effective to model the combinations of features.


SST-1 SST-2 QC NBoW (Kalchbrenner et al., 2014) 42.4 80.5 88.2 PV (Le and Mikolov, 2014) 44.6 * 82.7 * 91.8 * CNN-non-static (Kim, 2014) 48.0 87.2 93.6 CNN-multichannel (Kim, 2014) 47.4 88.1 92.2 MaxTDNN (Collobert and Weston, 2008) 37.4 77.1 84.4 DCNN (Kalchbrenner et al., 2014) 48.5 86.8 93.0 RecNTN (Socher et al., 2013b) 45.7 85.4 -RAE (Socher et al., 2011) 43.2 82.4 -MV-RecNN (Socher et al., 2012) 44.4 82.9 -AdaSent (Zhao et al., 2015) --92.4 GRNN (our approach) 47.5 85.5 93.8 Table 2: Performances of the different models.

dAG structure is relatively complicated.


For the same-period training data, the learner shows the same behavior as the boosting.

trAdaBoost (trAdaB) is presented by (Dai et al., 2007).


LDA and PAV-EM spent 15.2 and 13.3 seconds on average for processing the smallest set, "DiGeorge syndrome".

in this probabilistic framework, theme terms are iteratively selected and documents are assigned to a most likely theme.


We manually inspected the output of the classifier to identify possible issues.

the other pages in our sample do not follow a pre-defined pattern and present the person's biography in one or several sections at the same level of the other ones (see for instance https://en.


The key difficulty of this method is that calculating the gradient of the objective function needs to sum over exponentially many samples.

3 Learning and Inference Using (1), we obtain the difference between the log-probability of a correct derivation y c ijk ∈ Y i and a false one y f ijl ∈ Y i as: Note that the subtraction in (2) cancels the denominator of (1) which contains extensive computation.


For example, it is hard for our algorithm to relate the word "forfeits" to "minus", if it does not appear in the training corpus.

we simply evaluate all the y ijk ∈ Y i .


CC organizes classifiers along a chain and LEAD organizes classifiers in a Bayesian network.

jCC and jLEAD wins over CC and LEAD respectively, which shows the values of the proposed joint learning algorithm.


Consequently, when minimizing the global loss function, the weights of the k-th classifier are updated according to not only the loss of the k-th classifier but also the losses of the latter classifiers.

the joint algorithm minimizes this global loss function, as Eq.


In this subsection, we introduce architecture and loss function of our joint learning algorithm.

when minimizing the global loss function, the weights of the k-th classifier are updated according to not only the loss of the k-th classifier but also the losses of the latter classifiers.


K} and i ∈ {1 Having defined the document groups D P and the comparison criteria D C , we now construct a bipartite graph model used to perform comparisons.

document groups are considered more similar when they have similar sets of topics in similar proportions.


As previously, we observed significant computational complexity for traditional CR methods when applied to CCR setting making them far slower (6 − 7×) than C3EL.

for entities present in the KB, we observe an accuracy improvement of 0.5% over AIDA (refer Table 7).


The hyperedges also need to be established accordingly in order to properly model all possible mention and head combinations.

we randomly split the documents for each dataset into three portions: 80% for training, 10% for development, and the remaining 10% for evaluations.


We followed the description of Finkel and Manning (2009) to set up our experiments on the GE-NIA dataset.

we compared our system's performance with those of several baseline approaches.


We expect overall precision to increase if this extractor is removed.

type t KB matches t if it is either identical, a hypernym, or a hyponym of t. For each match, the KB lookup outputs t. If t KB is a hyponym of t, we additionally output t KB , that is, a type more specific than the one found by the extractor.


Sil (2013) also proposes jointly conducting NER and linking tasks.

jERL is able to model how consistent NER and linking's outputs are, and predict coherent outputs.


By finding and exploiting patterns in bilingual data, we want to provide an upper bound for this question: How much information does a human translator add to the original?

we must include those alignments at the beginning of the compressed file.


With a slide window of the distributed sentence representation, a neural network classifier is trained to evaluate the coherence of the text.

the sentence-level language model is another recurrent neural network that takes sentence represen-tation as input, and predicts the words in the next sentence.


multi-party dialogue or multi-party chat involves multiple interlocutors that may address one or more interlocutors during their turn.


The presence of such structures makes a powerful case that the general framework guiding the annotation of multi-party dialogues should take non-tree-like graphs as the basic form of discourse structures.

a turn can't be anaphorically and rhetorically dependent on a turn that comes after it.


All models presented in the previous subsections have in common that they do not rely on other features of the argumentation graph.

identifying the structure of argumentation according to our scheme involves choosing one segment as the central claim of the text, deciding how the other segments are related to the central claim and to each other, identifying the argumentative role of each segment, and finally the argumentative function of each relation.


the network structure is outlined in Figure 2.


We split the dataset into 30% and 70% for testing and training respectively.

ontoNotes covers various genres such as conversations, news-wire, and Weblogs, which provides distinctive variations of comparison structures in natural language.


One approach for pinpointing comparison sentences is to mine for some known patterns and train a classifier for distinguishing comparison and non-comparison sentences (Jindal and Liu, 2006b).

in this paper we present a novel semantic framework for representing the meaning of comparative structures in natural language, which models comparisons as predicate-argument pairs interconnected with semantic roles.


(2) The evidence for the taxonomic relation between 'fly-ing fox' and 'bat', though rare, is from trusted sites 9 which are maintained by scientists.


Traditional semantic parsers (Vo et al., 2015;Zettlemoyer and Collins, 2005) assign meanings to each word in a dictionary and combine the meaning of the words to characterize the complete sentence.

another challenge that needs to be addressed is the computation of similarity between complex concepts such as "size of class" and "20 students".


In the current version, the reasoning module has knowledge of a selected set of relations and the translation module tries to represent the clue as a conjunction of these relations.

1) each tuple in a logic grid puzzle will contain exactly one element from each category and 2) an element will belong to exactly one tuple.


It has been reported that the fast align approach is more than 10 times faster than baseline GIZA++, with comparable results in end-to-end French-, Chinese-, and Arabic-to-English translation experiments.

aligning words in a parallel corpus is a basic task for almost all state-of-the-art statistical machine translation (SMT) systems.


(Bach et al., 2011)) to update the cost of the search graph hypotheses.

@5 hypothesis a return to calm is precarious .


Finally, although N -best rescoring is used in this work to facilitate the discriminative training, other CTM's integration into SMT systems exist, such as lattice reranking (Auli et al., 2013) or direct decoding with CTM (Niehues and Waibel, 2012;Devlin et al., 2014;Auli and Gao, 2014).

for both conditions, the discriminative training gives a significant improvement, up to 0.7 BLEU score over the one only trained with NCE and up to 1.7 over the baseline system.


Arguably, the difference between the two conditions is much smaller than what was observed with the TED Talks task, due to the fact that the Patent-Abstract corpus used to discriminatively train the CTM only corresponds to a small subset of the parallel data.

in practice, the training data is successively divided into mini-batches of 128 sentences.


Suggestions from a machine translation system can increase the speed and quality of professional human translators (Guerberof, 2009;Plitt and Masselot, 2010;Green et al., 2013a, inter alia).

next, we trained a genrespecific translation model for each genre by first feeding the tune set and then the test set into our incremental adaptation learning method as a continuous stream of simulated post edits (+ genre TM).


As we do not have access to any dataset which provides scores to segments on the basis of translation quality, we used the WMT-13 ranks corpus to automatically derive training data.

the corpus acts as a scoring function for the available reference translation pairs, which gives a similarity score between a reference and a translation.


Most of the research work lies on deciding which aspects of quality are more relevant for a given task and designing feature extractors for them.

the CSLM features for each of the source and target segments are extracted using the procedure described in Section 3 with the CSLM toolkit.


3) may help correct some of the noisy triangulated scores, its effect is limited to phrase pairs appearing in both phrase tables.

the authors would like to thank Daniel Marcu and Kevin Knight for initial discussions and a supportive research environment at ISI, as well as the anonymous reviewers for their helpful comments.


As before, we added our supervised lexical weights as new features in the phrase table.

we therefore interpolated q with the triangulated word translation scoresŵ: To integrate the lexical weights induced by q β (Eq.


In this approach the segmentation process is able to exploit segmentation cues arising from the decoding process itself.

this process may also fail if the monotonic step would lead to the violation of L min .


Instead of forcing a monotonic decoding step in the event of a failure to find a segmentation point during the decoding, the hierarchical stream decoder directly eliminates hypotheses that would lead to such a failure.

this approach may be unsuitable for simultaneous interpretation where the machine translation system is required to provide translations within a reasonably short space of time after words have been spoken.


Overall, we find the results of both types of systems promising and we will consider how to introduce more diversity into the rules of string-to-tree systems.

the estimated distribution can be written as: In the same fashion as (Cui et al., 2010) do for the hierarchical case, we define a global rule selection model instead of a model that is local to the source side of each rule.


We created standard machine translation models between English and each one of Spanish, Italian, French and Dutch.

the motivation is not only to enable modeling in multiple languages, but also to enable modeling multilingual users who may express different sides of their personality in each language.


Language use is known to be influenced by personality traits as well as by sociodemographic characteristics such as age or mother tongue.

for this, we performed leave-oneout CV on each of the following three versions: native English, manually and machine translated into french.


This suggests that alternative methods should be used for our task.

to the best of our knowledge, MT domain adaptation does not extend to consider demographic or personality traits of the author.


it is first necessary to understand the inverse: the effect of current translation approaches on the computational recognition of these traits.


The reason for it to be low may be that discontinuous relations included by EXPAN-SION are rare so the frequent continuous relations dominate, just as for English.

hobbs (1985), Lascarides and Asher (1993) and Knott and Sanders (1998)).


Sanders (2005) proposes the causality-bydefault hypothesis, claiming that CAUSATION is the default discourse relation when processing discourse.

tEMPORAL.Asynchronous.succession) relation can appear between two clauses within a sentence.


One taxonomy is based on deictic continuity (Segal et al., 1991;Murray, 1997): continuity in the sense of Segal et al.

the results from M-all match the continuity hypothesis prediction that events in for-ward temporal order are easier to understand, thus less marked, than events in backward temporal order.


For instance, regardless of their gender, older deceivers use references to anxiety, money, and motion.

the learning trend suggests that most classifiers benefit from increasing amounts of training data.


If Q = 1, return an identity template, i.e., s, s such that s ∈ c 1 .

with PAIM and MaxEnt, which converge to the empirical distribution given sufficient data, MGL reserves a fixed amount of probability mass to unseen events.


Liguda & Pfeiffer (2012) propose modeling math word problems with augmented semantic networks.

here "$1: math.expression" means the first argument has type math.expression.


Please pay attention that our experimental results reported here are on number word problems.

for the other 2000 type names in the top 3000, we create classes and rules automatically, in the form of "class.TN → TN", where TN is a type name.


DOL enables function overloading (again borrowing ideas from programming languages).

another type of semantic parsing work (Zelle & Mooney, 1996;Zettlemoyer & Collins, 2005;Zettlemoyer & Collins, 2007;Wong & Mooney, 2007;Cai & Yates, 2013;Berant et al., 2013;Kwiatkowski et al., 2013;Berant & Liang, 2014) maps NL text into logical forms by supervised or semi-supervised learning.


A valid DOL tree must satisfy the type-compatibility property: For example, in Figure 2, the return type of nf.math.power is math.expression, which matches the second argument of vf.be.equ.

we also plan to study the mechanism of modeling relations and actions.


Most (if not all) previous symbolic approaches suffer from two major shortcomings.

it is still a big challenge nowadays to design algorithms to automatically solve even primary-school-level math word problems (i.e., math problems described in natural language).


The goal of sequence labeling is to assign labels to all elements in a sequence, which can be handled with supervised learning algorithms such as Maximum Entropy (ME) (Berger et al., 1996) and Conditional Random Fields (CRF) (Lafferty et al., 2001).

currently most of state-of-the-art methods for chinese word segmentation are based on supervised learning, whose features are mostly extracted from a local context.


Therefore, we usually need utilize the non-local information for more accurate word segmentation.

intuitively, if the LSTM unit detects an important feature from an input sequence at early stage, it easily carries this information (the existence of the feature) over a long distance, hence, capturing the potential useful long-distance information.


The dashed links represent the weight matrices from beginning to end are diagonal.

in the rest of the paper we will give more analysis based on the LSTM-1 with hyper-parameter settings as showing in Table 1.


For example, (Sun and Xu, 2011) enhanced their segmentation results by interpolating statistics-based features derived from unlabeled data into a CRF model.

to address the scarcity of manually segmented corpora, a number of semi-supervised CWS approaches have been intensively investigated in recent years.


The results presented in Table 4 indicate that SLBD demonstrates much stronger performance, primarily because these other methods were developed with a focus on SMT, which causes them to preferentially decrease the perplexity of the subsequent SMT steps rather than producing a highly accurate segmentation.

we perform a force decoding for the sentence tuple <w N +1 1 , e I 1 > and obtain the set of decoding paths P(w N +1 1 ), where each element acts as a decoding path that can translate w N +1 1 into e I 1 .


Sample combination has no or slight gain on BLEU score, in spite of the increase in grammar size.

more formally, the generative process is represented as follows: where s is source side terminals and t is target side terminals in phrase pair ⟨s/t⟩.


The combination of our Back with future score during slice sampling (+future) achieved further gains over the slice sampling without future scores, and slightly decrese the grammar size, compared to Back.

bi-parse the sentence pair in a bottom up manner.


For every possible link l (line 9), the algorithm produces a new alignment a and updates the phrase set by calling a procedure UPDATE(f , e, a, l, B, w) (lines 10-11).

using coverage to bridge word alignment and machine translation can hopefully benefit more languages.


If we know that the translation of "wèntí" is "issue", the relatedness between ("issue", "hold") and between ("issue", "position") can provide evidences that "hold" and "position" are the correct translations of "chíyǒu" and "lìchǎng", respectively.

we validate the effectiveness of our graphbased lexical selection framework on a hierarchical phrase-based system (Chiang, 2007).


We also use the following two notations: • The evidence vector V: an n-dimensional vector where the ith component V i is the evidence score contained in this node (if node i corresponds to a source word), or the evidence score from the related translations (if node i corresponds to a target translation).

we first identify source-side content word pairs using statistical metrics, and then keep word pairs with a high relatedness strength in the translation graph.


This enables BCorrRAE to produce desirable bilingual phrase structures from the perspective of word alignments.

to investigate the impact of embedding dimensionality on our BCorrRAE, we tried four different dimensions from 25 to 100 with an increment of 25 each time.


We were able to obtain an average gain of +0.3 BLEU points by training an NDAM v1 model over the selected data (see S v1+mml ).

for example, an Arabic-to-English SMT system trained by simply concatenating inand out-domain data translates the Arabic phrase " " to "about the problem of unwanted pregnancy".


A fifth to a quarter of the sentences in the Chinese news data that we analyze are translated to multiple sentences in English.

there are 301 test sentences, 511 training sentences in OpenMt and 2418 in MtC.


However, because MT decoding is inexact (beamsearch), many potentially promising derivations might be pruned before reaching the final beam (e.g., the partial derivation Figure 2).

in the rest of this section, we will explore three different methods to generate outside strings.


2 Related Work The idea of projecting annotated resources across languages using parallel data was first proposed by Yarowsky et al.

our assumption throughout will be that we have in our possession a single multilingual corpus (the Bible) consisting of about 200,000 tokens for several hundred languages languages, as well as reasonably accurate PoS taggers for about ten "resource-rich" languages.


The task of Arabic automatic diacritization is about the automatic restoration of the missing diacritics.

in example (d), our best system incorrectly changed the correct baseline analysis in agreement with the wrong dependency relation provided by the parser, which is the likely cause of error.


Most of the missing assignments are for foreign proper nouns, which often do not receive case markers.

we computed an oracular topline using the PATB gold case and state values in the retagging process.


This architecture is connected with previous work on incorporating word embeddings into a linear model.

deterministic transition-based parsers works by making a sequence of greedy local decisions (Nivre et al., 2004;Honnibal et al., 2013;Goldberg et al., 2014;Gómez-Rodríguez and Fernández-González, 2015).


Previous studies explored the trade-off between computational costs and parsing performance.

the only difference between them is l * (s,r) that can be calculated in previous derivations.


In online unsupervised DA, domain adaptation is performed incrementally as data comes in.

as Table 3 shows, the error rate for unknowns is greater than for unseens which is in turn greater than the error rate on known words.


We next try to directly learn trees containing the error suffixes (except for deleted tokens).

pora, its granularity increases when the error suffixes are interpreted in the syntactic context in which they occur.


A distributional profile (DP) of a word or phrase was first proposed in (Rapp, 1995) for SMT.

this results in a dramatic drop in coverage and many OOV phrases do not obtain any translation candidates.


To train the neural network, we optimize the following objective: where N is the number of training examples, trg i is the target vector representation for the ith example learned by RAE and p i is the output of the neural network for the source vector representation src i of ith example.

we randomly selected 5M examples as training set, 10k examples as development set and 10k examples as test set.


Unfortunately, syntactic or semantic parsers for many languages are not accessible due to the lack of labeled training data.

we further build a semantic nonterminal refinement model with semantic representations of nonterminals to compute similarities between phrasal substitutions and nonterminals.


The rest of this paper is organized as follows.

two features are added into the baseline system: 1.


the KenLM toolkit (Heafield et al., 2013).

computing the denominator is expensive for large vocabularies, as it requires computing the output for all words.


The integration of the NNs into the decoder is not trivial, due to the dependence on the target context.

the statistics for the bilingual corpora are shown in  table 2: Statistics for the bilingual training data of the IWSLt 2013 German→English, WMt 2015 German→English, and the DARPA BOLt Chinese→English translation tasks.


All other source words f , that are also aligned to e, are processed as if they were aligned to the artificial word σ .

as the MTUs are extracted sentence-wise, a word can potentially appear in multiple MTUs.


Finally, when used as an additional model in the log-linear framework of phrase-based decoding, an n-gram model trained on JTR sequences introduces only one single feature to be tuned, whereas the OSM additionally uses 4 supportive features (Durrani et al., 2013b).

the RNN scores are computed using approximations instead (Auli et al., 2013;Alkhouli et al., 2015).


AER global (location) 0.39 local-m (general) 0.34 local-p (general) 0.36 ensemble 0.34 Berkeley Aligner 0.32 Table 6: AER scores -results of various models on the RWTH English-German alignment data.

a neural machine translation system is a neural network that directly models the conditional probability p(y|x) of translating a source sentence, x 1 , .


All our models are trained on the WMT'14 training data consisting of 4.5M sentences pairs (116M English words, 110M German words).

we set out to evaluate the alignment quality using the alignment error rate (AER) metric.


(2) In SVM+Ngrams, we use bag-of-unigrams and bag-of-bigrams as features and train SVM classifier with LibLinear (Fan et al., 2008) 5 .

to this end, a simple strategy is ignoring the order of sen- Figure 3: Document modeling with gated recurrent neural network.


We conduct extensive experiments on four review datasets with two evaluation metrics.

the best method in each setting is in bold.


Importance of Fine-tuning in RNNs: Finally, in order to show the importance of fine-tuning the word embeddings in RNNs on our task, we present in Table 5 the performance of Elman and Jordan RNNs, when the embeddings are used as they are ('-tune'), and when they are fine-tuned ('+tune') on the task.

we also present an architecture to incorporate other linguistic features into RNNs.


One simple way to overcome this issue is to use a truncated BPTT (Mikolov, 2012) for restricting the backpropagation to only few steps like 4 or 5.

to give their network a better initialization, they learn word embeddings using a nonprobabilistic language model, which was trained on English Wikipedia for about 2 months.


This is particularly attractive for CCG, because the formalism's lexical and derivational ambiguity causes the parse charts to be very dense, in practice.

the features defined in Section 3.2 decompose over the rules of GENLEX -so it can be weighted with the globally trained feature weights.


CCG semantics Work on semantic parsing has mapped sentences onto semantic representations with latent CCGs (Zettlemoyer and Collins, 2009;Kwiatkowski et al., 2010;Kwiatkowski et al., 2013) for restricted domains.

the CCGbank dependencies used by Clark and Curran's model constrain all lexical and attachment decisions (only allowing 'spurious' derivational ambiguity) whereas our use of semantic dependencies models most of the syntactic parse as latent.


5 The advantages include: first, considering more possible alignments would increase the phrase coverage, especially when the training data is little; second, including the alignment from both directions would alleviate the error propagation caused by mis-aligned stop words (e.g., be, the in NL and stateid@1 in MRL ).

to this end, we enrich the nonterminals to capture the tree structure information, guiding the translation in favor of well-formed translations.


The rule-based text parsing baseline achieves a high precision, but at the cost of lower recall.

distance between the words of the concept nodes in the sentence.


The task is to select the correct interpretation given the visual context (c).

provided with a sentence, n candidate interpretations of that sentence and a video that depicts the content of the sentence, one needs to choose the interpretation that corresponds to the content of the video.


Model E does not have parameters for entity pairs, and instead has parameters for individual entities.

we evaluate our approach on the FB15k-237 dataset, a knowledge base derived from the Free-base subset FB15k  and filtered to remove highly redundant relations (Toutanova and Chen, 2015).


In the standard latent feature models discussed above, each textual relation is treated as an atomic unit receiving its own set of latent features.

while 26.6% of the validation set triples have textual mentions, the percentage with textual relations that have been seen in the training set is 18.4%.


We used our proposed tagger tagger in all experiments and results are reported for the English Penn Treebank.

the softmax over words is always performed on lowercased words.


Furthermore, each word is associated with a number of senses, the most plausible of which is selected dynamically during the composition process.

the results in table 1 show clearly that our model tends to group words that are both semantically and syntactically related; for example, and in contrast with the compared models which group words only at the semantic level, our model is able to retain tenses, numbers (singulars and plurals), and gerunds.


It is a merit of this proposal that, in cases like these, it is possible for one to train the generic model of Figure 2 on any large corpus of text, and then use the produced word vectors and compositional weights to initialize the parameters of a more specific version of the architecture.

(2013), for example, compute the conditional probability of observing words in a context around a target word (an approach known as the skip-gram model).


During the training of each model, we minimize the hinge loss in Equations 2 and 3.

a small corpus such as the BNC is sufficient for producing high quality syntax-aware word embeddings.


In each case, the T node on the right-hand side of these rules generates the first post (in terms of posting time) in that subtree.

the continuation rules, X → t X * , recursively subdivide the X subtrees into topics spanning fewer posts.


It is also possible to introduce a tree structure into the model that uses a binomial distribution to decide whether a word was drawn from common or non-common topics.

nevertheless, they require the same topics to be present in each collection.


sentence) level and the phrase level using 5-class fine-grained labels.

this variant has been successfully applied to semantic parsing (Yih et al., 2014) and information retrieval .


The resulting z[•] is the sum of 1, 2, and 3-gram features.

in addition, Kim (2014) combines CNNs of different filter widths and either static or fine-tuned word vectors.


The main contribution of this paper is to combine the generative model for online change points detection and a regression model learned from the weights of words in documents.

when change points are identified, each part of the time series is approximated by specified parametric models under the stationary assumptions.


After extracting texts from HTMLs, we tokenize the texts into words.

stationary parametric models are not appropriate to represent such dynamic non-stationary processes.


We use RE-VERB (Fader et al., 2011) to extract the triples (predicate + 2 Arguments).

at last, we use the Markov Logic Network(MLN) (Richardson and Domingos, 2006) to infer the correctness of the predicates in H. The MLN is constructed using the inference rules ("soft" rules) generated by aMIE system (Galárraga et al., 2013) on YaGO.


Through analysis above, in our work, we pro-  Inspired by the work of (Mintz et al., 2009), we make use of the external knowledge YAGO and borrow the distant supervision technique to mine implicit facts for the extracted predicates.

the predicates in the T -H pair should be matched to the YAGO facts for making advantage of the connection information.


Moreover, it is delightful that our work can achieve a rather good result with a relatively simpler model architecture.

the information provided by standard pretraining with unlabeled data is more general, while that of heterogenous resource is more relevant to our task, hence is more informative and evident.


We can explain this difference via the distinction between these two kinds of methods for performance improvement.

to get more complex features, we adopt a nonlinear transformation: where x t is the word representation of the t-th word, W 1 ∈ R n 1 ×n 0 , n 0 is the length of word representation, f is an activation function and we use tanh in our experiments, N is the number of words to be labeled in the sequence.


For each word to be labeled, we adopt linear transformation to get the output vector o t : where n 4 is the number of semantic role labels in IOBES tagging schema.

lSTM is designed to mitigate this problem.


Moreover, our model makes it convenient to introduce heterogeneous resource, which makes a further improvement on our experimental performance.

the resulting vector o t for the t-th word is of length n 4 , and each dimension corresponds to the score of a certain semantic role label.


With bidirectional RNN, the dependencies in a sentence from both directions can be captured, and with L-STM architecture, long-range dependencies can be well modeled.

as shown in Table 2, compared to standard pre-training, the influence of heterogenous data is more evident.


7) and (b) the damping factor λ of the word-topic graph model (Eq.(9)).

for estimating the topical transition probability P t (j|i) and the document-sensitive value R d (w i ), we employ GibbsLDA++ 4 , an LDA model using Gibbs Sampling technique for parameter estimation and inference (Griffiths, 2002).


Performance of the word-topic graph model.

negation focus annotation on this corpus is restricted to verbal negations (Blanco and Moldovan, 2011).


The same test confirmed that there are significant differences in the performance of the best model Wmask when using representations of different sizes (p < 0.01   comparisons showed that Wmask model significantly improves its performance (p < 0.01) when using word representations of increasing size (50, 100, 200 and 300 dimensions).

the words in a language can be combined into infinitely many distinct, wellformed phrases and sentences.


Knowledge Graphs (KGs) like WordNet (Miller, 1995), Freebase (Bollacker et al., 2008), and DBpedia (Lehmann et al., 2014) have become extremely useful resources for many NLP-related applications.

the previous studies focus only on nodes.


Empirically we find that a threshold between 0.02 and 0.1 yields a good performance for prerequisite prediction task.

concept  Table 4: RefD scores between "deep learning" and the concepts linked from it.


We further explore the performance of RefD through a case study for the concept "deep learning" (denoted as c ).

also we notice that concepts in the middle have RefD scores which are very close to 0, showing that there is no prerequisite relations between these concepts and "deep learning".


We evaluate 6 different instantiations of the proposed approach discussed in Section 3, including: 1. bestMT: set k = 1, when finding the translated phrase phr m for a Twitter phrase phr t (Equation (1)), before ranking target medical concepts for the translated phrase phr m using Equation (2).

when training the translator, we show the learner pairs of the Twitter phrases and descriptions of the corresponding SNOMED-CT concepts.


1 MCTest is an open-domain multiple-choice question answering task on fictional stories consisting of two datasets, MC160 and MC500.

concretely, on the first pass, we set the sliding window size to 2 tokens, and increment this size on each subsequent pass, up to a length of 30 tokens.


CCG has also been used for broad-coverage recovery of firstorder logic representations (Bos, 2008;Lewis and Steedman, 2013).

given a mini-batch size of M , we use the procedure SUB(D, i, M ) to get the i-th segment of the data D of size M .


there are 182 hotels if allowing dogs or not is not an issue .

the LSTM-based system described in this paper can deal with these problems automatically by learning the control of gates and surface realisation jointly.


02: Update parameters involved in Equ (3)(4) based on current word prediction.


Enumerating Expressions: For generating a next state S from S, we choose a pair of terms t i and t j in S and one of the four basic operations, and form a new term by combining terms t i and t j with the operation.

this theory naturally leads to a computational solution that we have shown to uniquely determine the solution -determine the arithmetic operation between any two quantities identified in the text.


Also, we add a feature based on whether the units of other quantities have better matches with question tokens (based on the number of tokens matched), and one based on the number of quantities which have the maximum number of matches with the question tokens.

they implicitly assume that similar equation forms have been seen in the training data.


This score can be interpreted as a conditional probability by applying a softmax operation (see Section 3.5).

since the parameters for all the model are determined by grid search, we can observe that CNNs cannot achieve competitive results compared to PCNNs when increasing the size of the hidden layer of convolutional neural networks.


Under the assumption that we use n filters ( , the convolution operation can be expressed as follows: The convolution result is a matrix C = s+w−1) .

we propose a piecewise max pooling procedure that returns the maximum value in each segment instead of a single maximum value.


Even finer information can be expressed by a word's role on the dependency path between entities.

embedding Models Word embeddings and compositional embedding models have been successfully applied to a range of NLP tasks, however the applications of these embedding models to relation extraction are still limited.


Roth and Woodsend (2014) considered features similar to ours for semantic role labeling.

we propose a general framework to construct an embedding of a sentence with annotations on its component words.


Other work has as-sumed that the order of two entities in a relation are given while the relation type itself is unknown (Nguyen and Grishman, 2014; Nguyen and Grishman, 2015).

in prior work both of above approaches are only able to utilize limited information, usually one property for each word.


Formally, we have where W in and W rec are weight matrices for the input and recurrent connections, respectively.

note that we did not try the latter three channels alone, because each single of them (e.g., POS) does not carry much information.


With this method we retrieve 89% of known speakers, of which 92% is assigned correctly.

our third extraversion prediction system is subordinate to how fictional characters are described and to the manners in which they behave.


We adapt the Stanford Named Entity Recognizer (Finkel et al., 2005) to consider titles (Mr., Mrs., Sir...) as a part of the name and to treat the first person I as a named entity.

introverts, on the other hand, seem to favor slow movements -while they are thinking, reflecting, creating, looking for explanations and find out solutions, they tend to lie down, sit or walk, eventually even sleep or snooze.


Hence we base our present study primarily on the previous NLP research on personality prediction of human individuals.

even the state-of-the-art NLP work rather describes personas of fictional characters by their role in the story -e.g., action hero, valley girl, best friend, villain etc.


SNT Our joint segmentation, normalization, and POS tagging model.


Using the same example shown in Figure 2, the following three actions can be applied for the character " ' big)": (1) APP("'(big)"), appending "'(big)" to the last word "-¨(yālí, pear)" in the informal labeled sequence, which remain with the same POS tag "NN".

li and Yarowsky (2008) classified them into four types: homophone, abbreviation, transliteration and others.


Given an input sentence x, the baseline segmenter finds a segmentation by maximizing: where Gen(x) denotes the set of all possible segmentations for an input sentence.

our model cannot normalise the word " Žv" in the experiment.


The content of Figure 2 provides an explicit indication of the actual features defined in the original treebanks (see Seddah et al.

this strategy becomes less intuitive when it comes to modeling free word order languages where word order and constituency should in principle be less informative.


Linear Scoring Features Our traditional linear scoring features in φ(h l − → m) are mainly drawn from previous work .

similar to previous work on low-rank tensor scoring models (Lei et al., 2014;Lei et al., 2015), we combine the traditional scoring and the low-rank tensor scoring.


For instance, there is no point to constructing features of the form {head POS=ADJ}∧{head POS=VERB} ∧ • • • ∧ {82A=SV} as the head POS takes a single value.

in this section, we describe our learning method.


In the multilingual transfer setting, however, we have some prior knowledge about legitimate feature combinations.

: where e 5 is the embedding that represents the delexicalized transfer results.


Results Results on the development set are given in Table 1 with our three decoding methods.

next, we compute the probability of the rule a[h] → x by counting how many times that rule appears in the training instances, and normalize by the total count of a[h] in the training instances.


Their method only relies on dense features, and is not able to automatically learn the most useful feature conjunctions to predict the transition action.

in addition, we set minibatch size to 20.


(2014) used the dense vectors (embeddings) to represent words or features and found these representations are complementary to the traditional discrete feature representation.

besides, the features of the selected nodes are just simply concatenated and then fed into neural network.


24 phone calls (24 operator channels and 24 customer channels) were used in the evaluation.

k , Θ) represent the emission probabilities that respectively model the dependency between latent words in two layers and the dependency between the observed word and the latent word in the lowest layer.


In fact, when we generated one billion words using a trained LWLM or trained h-LWLM, the number of observed trigrams in h-LWLM with 5 layers was 101M while the number of observed trigrams in non-hierarchical LWLM was 94M.

in fact, when we generated one billion words using a trained LWLM or trained h-LWLM, the number of observed trigrams in h-LWLM with 5 layers was 101M while the number of observed trigrams in non-hierarchical LWLM was 94M.


An N -best list can be included easily into the tuning and the decoding process of a statistical machine translation (SMT) system (Zhang et al., 2004).

this also requires decoding (translation) of a deep N -best list derived from the held-out set.


the order of the two nodes is sequentially close to each other, which can be formulated as follow: At last, we can formulate the objective function as follow: where parameter λ tunes the effect of the two parts and n is the quantity of BSUs in the final BSU semantic link network (after reduction).

the eligible action indicator is restricted to be a predicate verb; the eligible actor and receiver arguments are noun phrase.


Both of these systems need to select a subset of the large volumes of generated sentences.

as the BSU contains enough semantic and syntactic information, sentence can be generated efficiently according to the following rules:  Generate a Noun Phrase (NP) based on the actor argument to represent the subject, a NP based on the receiver argument to represent the object if present.


Since each node can be traversed only once, the following constraints must be satisfied.

these approaches are sentence rewriting techniques based on syntactical analysis without semantic analysis and abstract representation.


selecting a subset of sentences that maximizes an objective function under a length constraint, and use Integer Linear Programming (ILP) to solve it exactly (McDonald, 2007).

the choice of document frequency for weighting concepts is responsible for this as it generates many ties.


As this problem is NP-hard, pruning low-weight concepts is required for the ILP solver to find optimal solutions efficiently Li et al., 2013).

extending the model so that it provides a single solution can therefore not be done without introducing a second term in the objective function.


Table 1 presents the average number of optimal solutions at different levels of concept pruning.

as we will show through experiments, these multiple optimal solutions cause a substantial amount of variation in ROUGE scores, which, if not accounted for, could lead to incorrect conclusions.


Even finding annotators who are well-versed in the general art of rap lyrics can be challenging (Addanki and Wu, 2014).

in the context of an RNN we define this probability by: At each time-step the RNN computes f given an observation x and a previous state s. The input goes through a transformation where it passes through one or several hidden layers.


In terms of rap lyrics in general, a higher rhyme density is often better.

the results of our experiments show that, as an unsupervised, non-template model, the LStM model is better able to produce novel lyrics that also reflect the rhyming style of the target artist.


We follow the simple multiplicative approach described by Mitchell and Lapata (2008), where individual vectors of constituent tokens are multiplied together to produce the vector for a n-gram, i.e., where w is a n-gram composed of individual word tokens, i.e., w = w 1 w 2 .

looking ahead, we want to continue building on this work.


we will now explain how word embeddings can be incorporated into ROUGE.


Experimental Factors influencing Task Performance: In REAL, task success is defined as the ability to correctly identify an object, whereas in GIVE-2, task success refers to the successful completion of the navigation task.

in order to marginalise the effect of the scene complexity, we exploit the multiple available data points per image.


Dynamic LDA (DLDA) 2 : this is the dynamic LDA (Blei and Lafferty, 2006) where the topic-word distributions are linked across epochs based on the Markovian assumption.

we assume that the start date (or end date) of a storyline is the publication date of the first (or last) news article about it.


This work has been supported by the German Research Foundation as part of the Research Training Group "Adaptive Preparation of Information from Heterogeneous Sources" (AIPHES) under grant No.

we also compare results on DUC 2002 to    Table 5, because important information appears in the initial lines of news articles.


Lead performs best, because it selects the top five consecutive sentences, which are coherent as the original authors intended them to be.

we then remove nonalphabetical characters.


In DPP-based approaches, quality and selectional diversity correspond to vector magnitude and angle respectively.


Recently, recurrent neural network (RNN) have shown powerful abilities on speech recognition (Graves et al., 2013), machine translation  and automatic dialog response (Shang et al., 2015).

about 100 rules are used to extract high quality pairs.


The differences in average scores seen in the experiments are relatively small.

we use the PDTB class-level relations, of which there are four: expansion, comparison, contingency and temporal.


Here we focus narrowly on testing probabilistic generative models for dependency tree lineariza-tion.

here we focus narrowly on testing probabilistic generative models for dependency tree lineariza-tion.


Evolution of topics and their trends were studied on research corpora from NIPS (Wang and McCallum, 2006) as well as CiteSeer (He et al., 2009).


To that end, personalization is based on users' attributes, such as demographics (gender, age etc.

the answer is in the eye of the beholder.


Our method, denoted CTP (Collaborative Translational Preferences), is based on a k-nearest-neighbors approach for user-user CF.

yet, in our experiments, CTP turned out to be rather insensitive to their values.


As we shall see in Section 4, due to a limited search space, on-demand augmentation is much faster compared to algorithms in (Gardner et al., 2013;Gardner et al., 2014).

the run times, i.e, the time taken to perform an entire experiment for PRA-SVO and PRA-VS includes the time taken to augment NELL KB with SVO edges.


Corpus-driven approaches based on the distributional hypothesis therefore generally learn embeddings that capture both similarity and relatedness reasonably well, but neither perfectly.

although this behavior is sometimes desirable, it may in other cases be detrimental to downstream performance.


4 For relatedness-specific embeddings, the effect is less clear: joint learning performs comparatively much better.

with retrofitting the model first acquires good representations for frequent words from the raw text, after which it can better understand, and learn from, the information in the thesaurus.


ADDEVENTCOREF(y, c) 11: and Farkas, 2012).

unlike previous work (Berant et al., 2014;Li et al., 2013), we explicitly allow an event trigger to have multiple tokens, such as verb phrase 'look into' and compound proper noun 'World War II'.


Line 3 -7 implements the segment-based decoding, and line 8 -11 implements the best-first clustering.

the first stage alone is unlikely to detect E2 and E4 as events due to the difficulties described above.


They eliminate unknown symbols by training the system to recognize from where in the source text each unknown word in the target text came, so that in a postprocessing phase, the unknown word can be replaced by a dictionary lookup of the corresponding source word.

1st Symbol denotes the precision of the first pseudo-word symbol in an encoded rare word.


We demonstrated its improved performance as compared to the state-of-the-art Bayesian optimization algorithm and fast variants of Bayesian optimization on sentiment classification and QA tasks.

in this paper, we explore this problem from a different angle, and propose a multi-stage hyper-parameter optimization that breaks the problem into multiple stages with increasingly amounts of data.


When trained with λ on training data T train , the validation accuracy on T valid is denoted as L(λ, T train , T valid ).

in terms of time per iteration, the full Bayesian Optimization is the most expensive, taking more than twice amount of time over subset variant algorithm, while the multi-stage is 23% and 50% faster than standard Bayesian Optimization on QA and Yelp (Table 3), respectively, while maintaining the same accuracy as full Bayesian Optimization.


The argument space features (FS4) increase the performance in every combination.

we cast the task of identifying argument components as a sequence tagging problem and employ SVM hmm (Joachims et al., 2009).


As seen in Figure 6a, there is little difference in accuracy across sampling techniques: the location-based sample is slightly easier to geolocate at each usage bin, but the difference is not statistically significant.

for this reason, we bin users by the number of messages in our sample of their timeline, and report results separately for each bin.


We therefore focus on classification, employing the familiar and well-understood method of logistic regression.

this prior work did not consider the biases introduced by relying on geotagged messages, nor the consequences for geo-linguistic analysis.


Our semantic scores are the bag-of-words cosine and the latent semantic similarity scores for adjacent sentences (Guo and Diab, 2012).

in spite of this filtering, 7% of our 500 manually-labeled texts were identified as non-stories by our annotator.


We incorporate Prince's change-of-state formalization as well as Labov's definition of the MRE by modeling changes in story characteristics suggested by Prince, Polanyi, and Labov, such as measures of syntactic complexity and emotional content.

bag-of-words cosine similarity to the tl;dr performed poorly (RMSE of 13.2).


Heuristic Features: A general notion about the task is that suggestions contain some distinctive keywords like, suggest, recommend etc, and should be easily detectable using them.

we attribute this to the smaller size of electronics dataset.


Since human beings can inherently infer suggestions, the layman annotators considered both implicit and explicit form of suggestions as suggestions.

in the next section, we further annotate the sentences from these two datasets, for the current task.


So for any u's ancestor v, the probability of v being u's leader is: where P(v, u) is the set of nodes between v and u on v-to-u path of repost tree, i.e., P(v, u) = {w ∈ V |w is v's descendant and u's ancestor on T }.

table 1 lists the features we use for leader detection.


4: Initialize current walk="WALK-1" 5: for t = 1 to N and not converged do 6: if current walk=="WALK-1" then 8: Update pt(u → v) by equation (4)  9:Update Rt(v) as follows: Set current walk="WALK-2" 11: end if 12: if current walk=="WALK-2" then 13: Update Rv(v) as follows: Set current walk="WALK-1" 15: end if 16: end for 17: end for 18: Sort all v ∈ V by RN (v) in descending order 19: Pick the top-n messages as summary WALK-2 sampling from leader probabilities, it also reduces the risk of including real followers into summary.

this makes it possible to include true leaders misclassified as followers by leader detection module into summary.


Here we assume that a follower u's leader is its nearest ancestor leader on T as shown by the dotted lines in Figure 1.

a popular post usually attracts a large number of reposts.


In our zero anaphora resolution method, zero anaphoric relations are identified by propagating a subject through subject sharing paths in the SSPN.

for example, the subject of nomu (take) is omitted in example (1).


The results are shown in Table 5 and demonstrate that the performance of all the methods without Step 5 does not reach that of the baseline method in F-score.

we measured the performances of the baseline and our subject sharing recognition method using recall, precision and F-score for each of the three types of subject sharing relations, which are shown in Table 2.


When combining more than one subject sharing recognizer in our method, we construct the SSPN using the subject sharing relations recognized by at least one of those recognizers for transitive subject propagation.

also, if multiple subjects are detected for a predicate, we used the most likely subject to fill the subject position of the predicate, as in our method.


Concatenation and product yield two new feature mappings, respectively: These new representations live in a much lower dimensional real spaces: The various segment-pair representations that we derived from pairs of head words can be generalized to the case in which we keep all the words in each segment.

a recent example of such approach is the Hellinger PCa embeddings of (Lebret and Collobert, 2014) which were built using Principal Component analysis based on Hellinger distance as dimensionality reduction algorithm.


This work also leaves other important questions open.

one of the simplest representation one can construct for a pair of segments (S 1 , S 2 ) is to consider only their head words: h 1 = head(S 1 ) and h 2 = head(S 2 ).


One can thus use mapping Φ all,1,⊕ in lieu of Φ all,1,⊕ : It should be clear that Φ all,1,⊕ provides a normalized version of Φ all,1,⊕ as this latter mapping amounts to weighted versions of the former: Through the comparative framework described in section 3, our objective is to assess the usefulness of different vectorial representations for pairs of discourse segments.

an important appeal of these representations is that they are much less time-consuming to train than the ones based on neural language models while allowing similar performance (Lebret and Collobert, 2014).